Soccerment Joins MLS Innovation Lab with AI Analytics

0:00 OK, let's unpack this. Today's deep dive is taking us onto the pitch, specifically into the world of football or soccer as many of you know it, and exploring how cutting edge tech and AI are really changing the game. We're focusing on one company in particular, Soccer Mint, and looking at how their whole approach to data and analytics is shaking things up, including, you know, their recent selection for a Major League innovation program. That's right, Soccer Mint. They really position themselves as a sports tech company squarely focused on using AI data analytics mainly for performance analysis, player tracking in soccer, right?

0:41 Their mission, as they stated, is basically to speed up the adoption of predictive analytics, you know, across all levels of the sport. And that's kind of the mission for this deep dive, too. For you listening, We want to give you a shortcut sort of to understanding this whole revolution. We'll explore their specific tools, look at how they're measuring performance in totally new ways, ways that go, you know, way beyond just goals and assists and also what their collaboration with the league, like MLS means for the future of football.

1:07 So yeah, get ready to see the game through a pretty fascinating new lens. So let's dive into the tech itself. What are Sockerman score solutions? What allows them to actually do all this? Well, their offerings really center around two key things. First, getting the right data and 2nd making sense of it. OK, for the data collection part, their main product is called Exceed. Exceed and these are like smart shing guards, right? Exactly. These aren't your old school shing guards. Not at all. They're embedded with tech designed to collect really high quality, granular performance data directly from the player.

1:43 During games training both. During matches and training sessions, yeah. OK, so players wear these and they're capturing more than just, you know, impact protection. What kind of info are we talking about? It's sort of a dual layer of data they capture. You got the athletic metrics, things like toll distance covered, Yeah, top speed, number of sprints, acceleration. Standard stuff. Yeah, standard, but important. But critically, they also record and geolocate technical events right there on the pitch.

2:09 What does that mean? Geolocate technical events. It means knowing precisely where and when a pass was made, where a cross was delivered from, where a shot was taken, and even the power behind that shot. Wow. Yeah. That ability to tie the technical actions, the what to the location, the where, with that kind of precision, that's a really significant advancement in data collection right at the source. OK, capturing the what and the where of technical actions alongside the physical effort. That sounds complex.

2:38 What kind of tech is actually packed into something worn on a player's shin? Must be pretty small. It's quite sophisticated, yeah, especially for such a small and, you know, durable form factor. They contain multiple sensors, accelerometers, gyroscopes for tracking movement, a magnetometer and a high frequency GPS antenna operating at 20 Hertz, which helps capture that location data precisely. That makes sense. All of that plus a Bluetooth module to transfer the data, It's all integrated into a device that weighs less than 100 grams per shin guard under 100 grams.

3:08 Impressive. And they still have to meet, you know, strict safety standards, personal protective equipment rules, CE certification for strength, which meant a pretty significant development process. I've got. Yeah, including partnerships for things like the frame and the light guide. They mentioned working with Protolabs for injection molding, which apparently allowed them to do rapid prototyping and refine the design quickly. Right. That attention to the physical product, the durability, it really highlights the challenge of getting reliable data in, well, a pretty harsh sports environment.

3:42 Absolutely. So who is this X seed system really for? Is it just for the absolute elite top tier clubs with huge? Budgets. Well, while it's definitely valuable for elite teams, X Seed was actually developed specifically to try and make this kind of cutting edge tech more accessible. Accessible to who? To coaches, teams, clubs, even below that very top level, the data gets delivered through a pretty user friendly mobile app and a web dashboard. Offers clear visualizations, player rankings, even includes some virtual coaching features that use the data to give tailored feedback and advice.

4:16 Huh. Making advanced data more attainable for a wider audience? That feels, well, pretty significant for developing talent across the board, doesn't it? I think so, yeah. You mentioned 2 core solutions though. What's the second piece of their puzzle? That would be their X value platform. This is the advanced analytics engine. It's where the data collected by Exceed or maybe integrated from other sources gets processed. Right, the brains of the operation. Kind of, yeah. It's where the AI and machine learning really come into play, turning all those raw data points into hopefully actionable insights for clubs, academies, players, ultimately supporting more advanced predictive analytics.

4:53 OK, so exceed captures the Super detailed data X value makes that data intelligent. This leads us perfectly into the next question. Why do we even need these new ways to measure performance? We've had goals and assists forever. Why the big push for these advanced metrics? That's really the core problem. Soccer men and others in this space are trying to solve football. You know it's inherently a low scoring game. It's true. And the outcome of key events like whether a specific shot goes in or not can involve a pretty high degree of randomness.

5:28 Luck basically. Right the deflection, the incredible save. Exactly. So evaluating players or team performance based only on those rare, sometimes random events, it can lead to bias conclusions. Well, a player might just have a lucky streak right outperform their underlying ability for a short period. Or, conversely, a player could be consistently creating absolutely brilliant chances that their teammates just aren't finishing. OK, so traditional stats don't capture that nuance. Not fully. Clubs need more objective, more robust measures.

5:59 Things that aren't swayed so easily by short term results or that kind of volatility. And this is where probability and machine learning step in to smooth things out. Precisely By using really detailed event data, the context, the location, the type of every action, these machine learning models can move beyond just the binary goal or no goal outcome. They could assign a probability to an action based on what typically happens in similar situations across thousands, even millions, of historical data points.

6:30 So it gives a measure of the quality of the action. Exactly the underlying performance quality, rather than just the final result which might have been lucky or unlucky, it's much more objective. OK, let's unpack some of these specific metrics Sacrament uses then. Expected goals or XG. That's probably the one people have heard of most. What does it represent in their system? Right XG expected goals. They define it as a pre shot probability. It's all about quantifying the quality of a scoring chance before the shot is even taken.

6:57 Before the shot, OK. Yeah, it assigns A probability to each shot, estimating how likely it is to result in a goal. And this probability comes from a statistical model trained on hundreds of thousands of pass shots. What factors does it consider? Various contextual things. Shot location on the pitch. That's usually the biggest factor. Makes sense, closer is better. Generally, yeah. Also the type of pass that led to the shot, the game state, the body part use head, foot, the pattern play. Things like that.

7:25 So fundamentally, it's telling you how good the opportunity was regardless of who took the shot. Exactly right. Think of a shot from say just inside the penalty area, pretty central that might get a value around foot one XG meaning meaning that on average about 10% of shots taken from that specific situation typically end up as goals. OK but it's really vital to remember XG measures chance quality. It's not predicting if that specific shot will go in, it's not really measuring the individual finishers skill at that point.

7:56 It's real power as a predictive tool comes when you aggregate lots of shots over time, like a whole season or multiple seasons. Then you can see if a player or a team is consistently generating high quality scoring opportunities. Got it. And how do they handle penalties in XG? Penalties are usually handled separately or assigned a fixed value. Because there's such a unique high probability event, Suckerman's model assigns a fixed .78 XG for penalties based on the historical average conversion rate.

8:25 OK, .78 makes sense. So that's XG measuring chance quality? What about expected assists or XA? What's that metric capturing? Expected Assists XA tries to measure the probability of a pass becoming a goal assist. The whole idea is to credit the players who are creating scoring chances for their teammates through their passing ability. The creative players. Exactly. But this metric, well, it's seen a couple of different approaches in the analytics world. Oh, different ways to calculate it, yeah. A lot of models use what's called a shot centric approach.

8:57 Basically, whatever the XG value is of the shot that follows a pass, that XG value gets credited back to the passer as their XA. OK, seems logical. It does, but Sockerman uses a different method, a pass centric approach which they argue gives a more nuanced attribution of credit. Pass centric, how does that differ? OK, so Sockerman's model is trained on all completed passes, not just the ones that immediately lead to a shot. It evaluates whether the pass itself successfully moved the ball into a dangerous area of the pitch.

9:26 Regardless of what the receiver does next. Exactly, regardless of whether the receiver shoots or dribbles or passes. Again, they believe this gives a fair reflection of the passers contribution to creating danger. Can you give an example to make that clear? Yeah, they often use this goal. Romulu Lukaku scored for Inter Milan. Ivan Parisage played a fairly simple long pass from deep inside his own half up towards Lukaku near the halfway line. Lukaku. They went on this amazing solo run, beat several defenders and scored a goal.

9:56 The initial XG value of Lukaku's shot itself was around .11. Now in a typical shot centric model, Parisage would likely get the full .11 XA for that initial pass. Because it led to the shot. Right, but Sockerman's pass centric model looks at the pass itself. It recognizes that Parisage pass, while successful, didn't actually put Lukaku directly into a high probability scoring position. It was Lukaku who created most of the danger after receiving. It So what XA did their model give Parisage? Of very low value, just .001 XA.

10:31 Wow, big difference .11 versus .001. Huge difference and they argue that .0001 value much better reflects Parasix actual contribution to that specific goal, where the value is really added by Lukaku's individual skill after the pass. Their XA model, like the SG 1, is a statistical model trained on millions of completed passes. That distinction really is key, isn't it? Measuring the inherent danger of the pass itself. OK, so we have XG for shooting opportunity quality, XA for creative passing quality.

10:59 How do you combine those to look at a player's overall offensive contribution? Is there a metric? For that, yes, that's where they're metric called expected offensive value added or OVA comes. In OVA it basically combines a player's non penalty XG and their XA, the chances they create themselves through shooting and passing, and then it subtracts the XA they received from their teammates. So the formula is like non penalty XG plus XA generated, XA received. Pretty much, yeah. The idea is to try and isolate that player's individual impact on creating scoring chances.

11:31 How effectively they turn possession into dangerous situations. Exactly. It highlights how much a player contributes to chance creation through their own actions, both shooting and passing. But again, like XG and XA, this metric is focused on chance creation, not whether those chances actually get converted into goals. Right, the process, not the immediate outcome. And it also really relies on having that pass centric XA model for it to work correctly and not double count value. They pointed out, for example, that in the 20/20/21 season, across the top 7 European leagues, Lewis Muriel at Atlanta had a really high OVA, something like .73 per 90 minutes.

12:07 Indicating a strong individual offensive impact, creating lots of chances himself. OK, so those metrics, XGXAXOVA, they're largely focused on what happens before the ball is struck or the pass outcome. What about evaluating what happens after, particularly for shooting skill and goalkeeping? Right, that takes us to expected goals on target or X got TT. X got T, another expected metric. Yeah. So while XG is that pre shot probability, assuming an average shot placement, if it hits the target, X got T is a post shot probability.

12:42 It's calculated only for shots that actually are on target. And what extra information does it use? It incorporates the original XG value, the quality of the initial chance, plus the precise location where the ball crosses the goal line or where the keeper saves it within the frame. So it factors in the shot placement. How does that help us understand finishing or goalkeeping then? Well, the difference between the exigot value of a shot and its initial XG value gives you something called shooting goals added or SGA.

13:06 Shooting goals added. Yeah, this metric is specifically designed to measure finishing scale. It quantifies how much a player's shot placement actually increase the probability of scoring compared to what an average player might achieve from that same initial XG opportunity. So players who consistently play shots in corners or difficult spots for the keeper would show high SGA. Exactly. Sockerman's analysis has highlighted players known for precise finishing, like, you know, Lionel Messi or Ciro and mobile.

13:35 They tend to demonstrate high SGA per XG, meaning they consistently score more goals than expected, even after accounting for the quality of the initial chances. Because they're finishing is just that good. OK, that makes sense. For evaluating strikers, how does X dot T apply to goalkeepers? It's used directly to evaluate keepers through the goals prevented metric. This is calculated pretty simply. You take the total X dot T that a goalkeeper has faced from all the shots on target against them, and you subtract the number of goals they actually conceded from those shots.

14:04 So ex Gotti face minus goals conceded equals goals prevented. Exactly. It provides a much more objective measure of pure shot stopping skill. It shows whether a keeper is saving more shots than an average keeper would be expected to save given the difficulty in placement of the shots they faced. Who stands out there? Yano Black is often the prime example highlighted in these discussions. Their analysis credited him with preventing something like almost 35 goals between 20/17/18 and the time of the analysis, and he was preventing about .28 goals per ex guy T faced in that period, which is really impressive.

14:40 Wow, that's a powerful way to quantify a goalkeeper's value beyond just clean sheets focusing on the difficulty of the saves. OK. Any other crucial passing metrics we should cover? Yes, one more important one is expected passes or expected. Passes OK, similar concept to the others. It's a model trained on millions of passes, and it calculates the probability of any given pass being successfully completed. Based on what? Based on factors like its length, its direction, the positions of players around it, game pressure, various contextual factors.

15:09 But hang on, we already have simple pass completion percentage stats. Why do we need X pass? What does it add? That's a great question. It adds context about difficult. Simple completion percentage treats all completed passes equally. Right. A5 yard sideways pass counts the same as a 40 yard diagonal ball through traffic. X pass tells you how difficult the attempted passes actually were. It helps identify players who aren't just completing lots of easy passes, but who are consistently completing the more difficult, riskier passes at a higher rate than you'd expect on average.

15:43 How do you see that in the numbers? You see it when a player's actual number of completed passes is significantly higher than their total X pass value for all the passes they attempted. They're basically outperforming the expected completion rate based on the difficulty profile of their passing. Any examples? Players like Tony Cruz or Marco Varadi are often cited as classic examples of midfielders who excel here. They consistently show an ability to successfully execute challenging progressive passes more often than the average player would.

16:12 Cruz for instance I think of the 20/20/21 season should something like a 5.19% over performance compared to his ex pass value. That's fascinating. It really highlights the difference between, say, a player who just keeps possession with safe passes versus one who actively tries and succeeds at making those difficult line breaking passes. Exactly. Gives you a much better sense of their true passing ability under pressure. Okay, this is all incredibly insightful about on field performance. Let's shift gears a bit.

16:41 How does all this detailed data and these advanced analytics impact the business side of football, specifically thinking about player recruitment? Yeah. This is where it gets really interesting from a market perspective, because despite the enormous amounts of money sloshing around at football, the transfer market is still characterized by, well, significant inefficiencies. There's still considerable room for arbitrage. Arbitrage meaning finding value that others miss. Essentially, yeah, identifying and acquiring undervalued players.

17:12 And this doesn't just apply to potential transfer fees, although it does there too. It applies very clearly to player salaries as well. So you're saying clubs could potentially find players who are performing at a really high level based on these objective metrics, but who are earning significantly less than other players with comparable performance? Absolutely. Their analysis suggests exactly that. There are players on relatively modest salaries who are performing at levels comparable to or maybe even exceeding players earning exponentially more money.

17:41 Why does that inefficiency still exist? You'd think in such a high stakes, increasingly data aware environment, performance and salary would align more closely. Well, Sockerman points to a couple of key reasons. A primary one they suggest is that data analytics, while definitely growing it, isn't yet universally or perhaps deeply enough integrated across all clubs decision making processes. So not everyone is using it effectively yet. Right. Or consistently enough to fully link objective performance data to salary negotiations and valuations across the board.

18:15 They also mentioned identifying some structural issues within the market itself. Even for players who are supposedly in their prime years, say between 25 and 30, where you might expect the market to be more efficient and settled. Apparently it's not always the case. Interesting. So for a club that is willing to fully embrace this data-driven approach, it potentially provides a real competitive advantage in recruitment. Precisely. They frame it as arguably the single clearest way for clubs to consistently gain an edge in the market, right?

18:47 Now how so? Well, by having access to better, more objective information through things like Exceed and the advanced metrics from XW, and crucially, having the analytical tools and expertise to extract actionable intelligence from it, clubs could potentially make much smarter recruitment decisions. They can capitalize on opportunities that opponents relying more on, say, traditional scouting or subjective judgments, might completely miss. OK. And how does Soccer Mint specifically use their data to try and identify these hidden gems or undervalued players?

19:17 What's their method? They approach this by directly comparing a player's performance against their net salary. For performance, they use their own proprietary metric, the Sacrament Performance Rating, or SPRS. PR. Importantly, this SPR metric is adjusted for the specific league level the player competes in, so that allows for, you know, better like for like comparisons across different competitions. Right, because performing well in one league isn't the same as another. Exactly. And then for the salary data, they integrate information from external sources, specifically mentioning capology, which tracks football finances.

19:53 So visually, are they just plotting performance on one axis and salary on the other looking for outliers? Essentially, yes. They plot players as PR against their salary, and they often use a logarithmic scale for salary. Which makes sense because the range of wages in football is just enormous, right? From modest earners to superstars. Right, a log scale compresses that huge range. So what are they looking for on that plot? They're looking for players who fall into that potential sweet spot, high up on the performance scale, a high SPR, but relatively low down on the salary scale.

20:23 The bargains, basically. The potential bargains, yeah, but they have a more formal way to quantify undervalued beyond just eyeballing the chart. How do they do that? First, they segment players by position Goalkeeper, defender, midfielder, forward. Because you need to account for ositional differences in both erformance profiles and tyical salary ranges. Makes sense? Then, using statistical techniques like robust linear regression, they establish a typical trend line for each position. This line basically shows the expected salary for a player given their SPR within that position group.

20:57 OK. So the line represents the market rate for performance. Kind of, yeah. The general relationship between performance and pay for that position. Undervalued players are then identified as those whose data points fall significantly below that established trend line. How? Far below. They actually quantify that vertical distance below the line and normalize it into an undervalue index, often indexed to 100 for easy comparison. They focus their search particularly on players. Their algorithm flags as true outliers, often defined as having an SPR above a certain threshold, say 50, and a high undervalue index score.

21:32 And did their deep dive into players aged 2530, the prime years confirm this inefficiency exists? Yes, they explicitly stated that their analysis of that age group confirmed that plenty of this market inefficiency does indeed exist. It's not just about finding young, unproven talent. That's a really clear methodology. Have they seen players identified using this approach actually make significant moves? Does it work in practice? Yes they have. They've highlighted specific examples of players they flagged as undervalued hidden gems based on this SPR versus salary analysis who subsequently moved to larger clubs, like Who They mentioned Steven Berquise moving to AX and Pierre Lee's Malou transferring into Nord City as examples of players fitting that profile who made moves after being identified.

22:16 That's pretty powerful validation for their approach, finding inefficiency and seeing it potentially corrected by the market later. Exactly. And they've also tried to make this more accessible for their own users now. They mentioned integrating the salary data directly from capology into their analytics platform for subscribers to their analyst pluses tier. So clubs using their platform can run these kind of undervalue analysis themselves. That seems to be the idea, yeah. OK, that's fascinating.

22:44 Which brings us right back around to the news we touched on at the very start. Sockerman was selected for the MLS Innovation Lab. How does all this connect? Is this why MLS pick them? It absolutely connects. I mean, being selected for the MLS Innovation Lab is, it's a direct recognition of the potential value that Sockerman's technology and analytics, everything we've just discussed, bring to the sport. It's a big deal getting into something like. That Oh, definitely. They were chosen for the program's second cohort, the one starting in 2025, after what sounds like a pretty rigorous global evaluation process.

23:19 MLS said. They reviewed hundreds of companies. Wow. And what is the MLS Innovation Lab specifically trying to achieve? What's its purpose? The program is designed by MLS itself to help shape the future of sports, basically, and also drive the league's own growth. They do this by identifying, testing and then nurturing promising startups and advanced technologies. And MLS has specific areas they're focusing on. Yes, they defined key priority areas that they want this cohort of companies to help enhance.

23:51 Those are fan engagement, media technology and critically for soccer men on field player performance. Right, which is exactly soccer men's wheelhouse, as we've been discussing. Precisely. It's a perfect fit on paper. So getting selected gives soccer men a direct pathway to actually test their tools exceed X value within the real MLS environment. Exactly that. It provides soccer men with a really significant opportunity to test and showcase their innovations in real world MLS settings right across the league's ecosystem.

24:19 What kind of setting? They mentioned things like major youth development events, MLS Knickers Tea Fest, the Generation Agitus Cup, the MLS Knickers Tea Cup. These are big events with lots of teams, lots of players. It offers a great platform to prove the value and scalability of their tech at a pretty large stage. That does sound like a monumental step for a company like Soccer. Mint it. Certainly seems like it. Aldo Commie, who's Soccer Mint CEO, described it as a significant milestone for them.

24:47 He also expressed pride in being part of the cohort and interestingly noted they were the first Italian company ever selected for the program. First Italian company, huh? And what was the perspective from the MLS side? What did they say? Chris Schlosser, who's the SVP of emerging Ventures at MLS, commented on it. He acknowledged the success they'd already had with the first cohort of the lab and expressed real excitement about collaborating with Soccer Mint and the other companies in this new group.

25:14 His quote specifically mentioned the goal is to Co create new ideas and push the boundaries in the game of soccer. Co create new ideas that sounds very collaborative. What are the potential outcomes for companies like soccer meant that go through this innovation lab, what happens at the end? Well, the program culminates in opportunities to really present their capabilities. Companies in the cohort might get to present directly to MLS executives, even team owners, at an event called the Future of the Game Showcase.

25:42 When does that happen? It's typically held during the MLS All Star Game activities, so high visibility. Definitely. And what could come from that exposure? Potentially it could lead to long term strategic partnerships either with the league itself or with individual MLS clubs, and maybe even potential investment opportunities down the line. It effectively provides a pathway for potentially much deeper integration into the whole MLS structure. It really underscores MLS's broader strategic focus on tech and innovation, doesn't it?

26:12 Fits right alongside things like their big partnership with Apple for MLS season pass or their docu series work. Absolutely. It shows they're actively looking to integrate advanced technology, pretty much all facets of their operation from what happens on the field to the front office decision making and how they engage with fans. OK, so bringing this whole deep dive full circle back to you listening right now, think about the impact of this technological revolution happening in football. How does this big shift towards detailed data, advanced analytics, how does it change how you might follow your favorite team or how you evaluate individual players, or maybe just how you think about the game itself?

26:53 Yeah, it's clearly moving beyond just counting goals or assists, isn't it? Towards these much more nuanced probabilistic measures of performance and potential. Definitely. We've really explored how companies like Sockerman are using that incredibly detailed data collection through tools like the XE Shing Guards and then applying advanced machine learning models in platforms like XVU to create all these metrics. We talked about XG XE XE, Xova XE, Goody TX Pass, which aim to provide much more objective insights into player performance and contribution.

27:23 And we saw how that data can then be leveraged potentially to identify those market inefficiencies in player recruitment. Yeah, finding the hidden. Gems. Well, ultimately touching the eye of major leagues like MLS and leading to big opportunities like their Innovation Lab program. It's a fascinating conversions of sport, tech and business. Absolutely. And maybe that leaves us with one final thought for you to consider. As technology continues to integrate ever more deeply with sports, influencing everything from training and tactics to scouting and player evaluation, how might the very definition of talent OR value in a player continue to evolve?

27:59 How much will it be shaped by this increasing depth and maybe increasing accessibility of data? What stands out to you the most about this whole data revolution that's transforming football?